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22
Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction
"... Systems projecting a continuous n-dimensional parameter space to a continuous m-dimensional target space play an important role in science and engineering. If evaluating the system is expensive, however, an analysis is often limited to a small number of sample points. The main contribution of this p ..."
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Cited by 37 (5 self)
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Systems projecting a continuous n-dimensional parameter space to a continuous m-dimensional target space play an important role in science and engineering. If evaluating the system is expensive, however, an analysis is often limited to a small number of sample points. The main contribution of this paper is an interactive approach to enable a continuous analysis of a sampled parameter space with respect to multiple target values. We employ methods from statistical learning to predict results in real-time at any user-defined point and its neighborhood. In particular, we describe techniques to guide the user to potentially interesting parameter regions, and we visualize the inherent uncertainty of predictions in 2D scatterplots and parallel coordinates. An evaluation describes a realworld scenario in the application context of car engine design and reports feedback of domain experts. The results indicate that our approach is suitable to accelerate a local sensitivity analysis of multiple target dimensions, and to determine a sufficient local sampling density for interesting parameter regions. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation—Line and curve generation 1.
Interactive visual analysis of multi-faceted scientific data
- Dept. of Informatics, Univ. of
"... Abstract—Visualization and visual analysis play important roles in exploring, analyzing and presenting scientific data. In many disciplines, data and model scenarios are becoming multi-faceted: data are often spatio-temporal and multi-variate; they stem from different data sources (multi-modal data) ..."
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Cited by 25 (4 self)
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Abstract—Visualization and visual analysis play important roles in exploring, analyzing and presenting scientific data. In many disciplines, data and model scenarios are becoming multi-faceted: data are often spatio-temporal and multi-variate; they stem from different data sources (multi-modal data), from multiple simulation runs (multi-run/ensemble data), or from multi-physics simulations of interacting phenomena (multi-model data resulting from coupled simulation models). Also, data can be of different dimensionality or structured on various types of grids that need to be related or fused in the visualization. This heterogeneity of data characteristics presents new opportunities as well as technical challenges for visualization research. Visualization and interaction techniques are thus often combined with computational analysis. In this survey, we study existing methods for visualization and interactive visual analysis of multi-faceted scientific data. Based on a thorough literature review, a categorization of approaches is proposed. We cover a wide range of fields and discuss to which degree the different challenges are matched with existing solutions for visualization and visual analysis. This leads to conclusions with respect to promising research directions, for instance, to pursue new solutions for multi-run and multi-model data as well as techniques that support a multitude of facets. Index Terms—Visualization, interactive visual analysis, multi-run, multi-model, multi-modal, multi-variate, spatio-temporal data. 1
Tuner: Principled parameter finding for image segmentation algorithms using visual response surface exploration
- IEEE Transactions on Visualization and Computer Graphics
, 2011
"... Abstract—In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage techn ..."
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Cited by 22 (6 self)
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Abstract—In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the ”goodness ” of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans. Index Terms—Parameter exploration, Image segmentation, Gaussian Process Model. 1
The four-level nested model revisited: blocks and guidelines
- In Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization, BELIV ’12
, 2012
"... We propose an extension to the four-level nested model for design and validation of visualization systems that defines the term “guide-lines ” in terms of blocks at each level. Blocks are the outcomes of the design process at a specific level, and guidelines discuss re-lationships between these bloc ..."
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Cited by 7 (2 self)
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We propose an extension to the four-level nested model for design and validation of visualization systems that defines the term “guide-lines ” in terms of blocks at each level. Blocks are the outcomes of the design process at a specific level, and guidelines discuss re-lationships between these blocks. Within-level guidelines provide comparisons for blocks within the same level, while between-level guidelines provide mappings between adjacent levels of design. These guidelines help a designer choose which abstractions, tech-niques, and algorithms are reasonable to combine when building a visualization system. This definition of guideline allows analysis of how the validation efforts in different kinds of papers typically lead to different kinds of guidelines. Analysis through the lens of blocks and guidelines also led us to identify four major needs: a definition of the meaning of block at the problem level; mid-level task taxonomies to fill in the blocks at the abstraction level; re-finement of the model itself at the abstraction level; and a more complete set of guidelines that map up from the algorithm level to the technique level. These gaps in visualization knowledge present rich opportunities for future work.
Visual parameter space analysis: A conceptual framework
- IEEE TVCG (Proc. InfoVis
"... Abstract—Various case studies in different application domains have shown the great potential of visual parameter space analysis to support validating and using simulation models. In order to guide and systematize research endeavors in this area, we provide a conceptual framework for visual paramete ..."
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Cited by 3 (0 self)
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Abstract—Various case studies in different application domains have shown the great potential of visual parameter space analysis to support validating and using simulation models. In order to guide and systematize research endeavors in this area, we provide a conceptual framework for visual parameter space analysis problems. The framework is based on our own experience and a structured analysis of the visualization literature. It contains three major components: (1) a data flow model that helps to abstractly describe visual parameter space analysis problems independent of their application domain; (2) a set of four navigation strategies of how parameter space analysis can be supported by visualization tools; and (3) a characterization of six analysis tasks. Based on our framework, we analyze and classify the current body of literature, and identify three open research gaps in visual parameter space analysis. The framework and its discussion are meant to support visualization designers and researchers in characterizing parameter space analysis problems and to guide their design and evaluation processes. Index Terms—Parameter space analysis, input-output model, simulation, task characterization, literature analysis. 1
Guided Discovery of Interesting Relationships Between Time Series Clusters and Metadata Properties
"... joern.kohlhammer ..."
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Progressive High-Quality Response Surfaces for Visually Guided Sensitivity Analysis
"... In this paper we present a technique which allows us to perform high quality and progressive response surface prediction from multidimensional input samples in an efficient manner. We utilize kriging interpolation to estimate a response surface which minimizes the expectation value and variance of t ..."
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In this paper we present a technique which allows us to perform high quality and progressive response surface prediction from multidimensional input samples in an efficient manner. We utilize kriging interpolation to estimate a response surface which minimizes the expectation value and variance of the prediction error. High computational efficiency is achieved by employing parallel matrix and vector operations on the GPU. Our approach differs from previous kriging approaches in that it uses a novel progressive updating scheme for new samples based on blockwise matrix inversion. In this way we can handle very large sample sets to which new samples are continually added. Furthermore, we can monitor the incremental evolution of the surface, providing a means to early terminate the computation when no significant changes have occurred. When the generation of input samples is fast enough, our technique enables steering this generation process interactively to find relevant dependency relations.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2014.2346321, IEEE Transactions on Visualization and Com
"... Abstract—Various case studies in different application domains have shown the great potential of visual parameter space analysis to support validating and using simulation models. In order to guide and systematize research endeavors in this area, we provide a conceptual framework for visual paramete ..."
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Abstract—Various case studies in different application domains have shown the great potential of visual parameter space analysis to support validating and using simulation models. In order to guide and systematize research endeavors in this area, we provide a conceptual framework for visual parameter space analysis problems. The framework is based on our own experience and a structured analysis of the visualization literature. It contains three major components: (1) a data flow model that helps to abstractly describe visual parameter space analysis problems independent of their application domain; (2) a set of four navigation strategies of how parameter space analysis can be supported by visualization tools; and (3) a characterization of six analysis tasks. Based on our framework, we analyze and classify the current body of literature, and identify three open research gaps in visual parameter space analysis. The framework and its discussion are meant to support visualization designers and researchers in characterizing parameter space analysis problems and to guide their design and evaluation processes. Index Terms—Parameter space analysis, input-output model, simulation, task characterization, literature analysis. 1
A. Kerren and S. Seipel (Editors) Visual Parameter Optimization for Biomedical Image Analysis: A Case Study
, 2012
"... The conventional approach for parameter optimization of biomedical image analysis algorithms is to tweak parameters by trial-and-error. This presents a challenge: parameter space is often inadequately explored and, consequently, output quality suffers. Interactive visualization can alleviate this pr ..."
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The conventional approach for parameter optimization of biomedical image analysis algorithms is to tweak parameters by trial-and-error. This presents a challenge: parameter space is often inadequately explored and, consequently, output quality suffers. Interactive visualization can alleviate this problem but has not been widely adopted. Moreover, few examples of the successful application of visualization for parameter optimization of image analysis algorithms have been published. To address this and to illustrate the potential usefulness of interactive visualization, we present a case study. A multidisciplinary team developing novel image segmentation software for histopathology was observed. Within the context of our study, our hypotheses were confirmed: (1) using interactive visualisation, participants considered larger parts of parameter space than they had previously by trial-and-error; (2) participants gained a better understanding of their algorithm (an unknown logic error and errors in its implementation were discovered); and (3) participants achieved higher quality output. Our work is also an example of the value of case studies in iterative design. We describe how a valuable additional requirement was revealed (the importance of derived measures) and how our visualization method was extended to cater for this.
